A novel Facial Recognition technique with Focusing on Masked Faces

TL;DR

This paper introduces MUFM, a new facial recognition model that effectively identifies individuals with and without masks by leveraging transfer learning, cosine similarity, and a diverse dataset, addressing a key challenge in security applications.

Contribution

The study presents a novel face recognition approach using cosine similarity with transfer learning on VGG16, specifically designed for masked and unmasked face matching, which was not previously explored.

Findings

Effective identification of masked and unmasked faces using MUFM

High accuracy demonstrated across diverse datasets

Addresses a critical limitation in traditional facial recognition systems

Abstract

Recognizing the same faces with and without masks is important for ensuring consistent identification in security, access control, and public safety. This capability is crucial in scenarios like law enforcement, healthcare, and surveillance, where accurate recognition must be maintained despite facial occlusion. This research focuses on the challenge of recognizing the same faces with and without masks by employing cosine similarity as the primary technique. With the increased use of masks, traditional facial recognition systems face significant accuracy issues, making it crucial to develop methods that can reliably identify individuals in masked conditions. For that reason, this study proposed Masked-Unmasked Face Matching Model (MUFM). This model employs transfer learning using the Visual Geometry Group (VGG16) model to extract significant facial features, which are subsequently classified utilizing the K-Nearest Neighbors (K-NN) algorithm. The cosine similarity metric is employed to compare masked and unmasked faces of the same individuals. This approach represents a novel contribution, as the task of recognizing the same individual with and without a mask using cosine similarity has not been previously addressed. By integrating these advanced methodologies, the research demonstrates effective identification of individuals despite the presence of masks, addressing a significant limitation in traditional systems. Using data is another essential part of this work, by collecting and preparing an image dataset from three different sources especially some of those data are real provided a comprehensive power of this research. The image dataset used were already collected in three different datasets of masked and unmasked for the same faces.